Environmental Assessment of the Integration of Amine-based CO2 Capture Unit to Coal-Fired Power Plants for Greenhouse Gas Mitigation
Bibliographic record
Abstract
Coal-fired power plants produce flue gas containing a number of air pollutants including carbon dioxide (CO2), a major greenhouse gas contributing to climate change. Capture of CO2from the flue gas at the coal-fired power plants is one of the strategies to help Canada reduce its CO2emissions, achieve its Kyoto protocol reduction target, and also ensure the continuation of fossil fuel utilization. This paper assessed potential environmental impacts of the amine-based CO2capture unit which is integrated to the coal-fired power plants for post-combustion treatment of flue gas. The assessment was performed by identifying pollutants and their emission sources, reviewing the toxicology, potential implications to the human health and the environment, as well as related environmental laws, and ultimately providing recommendations of means to minimize such impacts. The analysis shows that while installing a CO2capture unit leads to an environmental benefit through a reduction of CO2emission in the atmosphere, it may create unintentional and potential burdens to the human health and the environment through four pathways including treated gas, process wastes, fugitive emissions, and accidental emissions. Such burdens nevertheless can be predetermined and properly mitigated through a well-established environmental management program and mitigation measures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".